MOOCs and Machines

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When Harvard University and the Massachusetts Institute of Technology sent ripples through the higher education world last week by announcing edX, a joint platform for massive online versions of their courses, many observers took it as a boon for access. And indeed, edX -- and other massive open online course (MOOC) projects, such as Coursera and Udacity, stand to give anyone with an Internet connection access to professors and courses that have been out of reach in the past.

But the piece of the announcement that might have caught the attention of learning scientists was the part that cast edX as a research project. “MIT and Harvard will use the jointly operated edX platform to research how students learn and how technologies can facilitate effective teaching both on-campus and online,” the universities said.

Anant Agarwal, the president of edX, said the scale of the courses, along with the data-rich environments in which they will be held, should enable researchers to glean “How people are learning, what works and what doesn’t.” The founders of another MOOC platform, Coursera, said they plan to work with their own data and university partners to “understand human learning at a scale and depth that has been never been possible before."

They are not the first to explore these questions. They are not even the first to do so by crunching data from free, online courses. Candace Thille has been doing similar work for years as director of the Open Learning Initiative at Carnegie Mellon University. In a series of studies, Thille has demonstrated that machine-guided learning, in concert with live instruction, can cut in half the time it takes students to master certain concepts.

With the MOOC projects and MIT and Harvard having thrust machine learning back into the spotlight, Inside Higher Ed tech reporter Steve Kolowich talked to Thille in a podcast about how open, online courses and big data might change what we can learn about learning.